Potential Biomarkers Selection for Bipolar Disorder Identification

Li, Yuanzhang; Yolken, Robert H.; Cowan, David N.; Boivin, Michael; Niebuhr, David W.

doi:10.22158/rhs.v2n3p262

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…All control subjects were matched to their cases on race, branch of military service, date of birth (+/‐12 months), and date of military enlistment (+/‐12 months). Further details about the design, methods, and medical implications of the study can be found in the work of Li et al There are 56 complete subjects in the data used in this section. Among the 64 biomarkers recorded for each subject, we identified seven pairs strong correlated with absolute values of Pearson correlation coefficients over 0.7 and 50 pairs moderately correlated with absolute values of Pearson correlation coefficients over 0.40.…”

Section: Analysis Of the Bipolar Study For Femalesmentioning

confidence: 99%

“…All control subjects were matched to their cases on race, branch of military service, date of birth (+/-12 months), and date of military enlistment (+/-12 months). Further details about the design, methods, and medical implications of the study can be found in the work of Li et al 28 There are 56 complete subjects in the data used in this section. Among the 64 biomarkers recorded for…”

Section: Data Introduction and Feature Space Decompositionmentioning

confidence: 99%

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Decomposition feature selection with applications in detecting correlated biomarkers of bipolar disorders

Huang

Liang

et al. 2019

Statistics in Medicine

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Feature selection is an important initial step of exploratory analysis in biomedical studies. Its main objective is to eliminate the covariates that are uncorrelated with the outcome. For highly correlated covariates, traditional feature selection methods, such as the Lasso, tend to select one of them and eliminate the others, although some of the eliminated ones are still scientifically valuable. To alleviate this drawback, we propose a feature selection method based on covariate space decomposition, referred herein as the “Decomposition Feature Selection” (DFS), and show that this method can lead to scientifically meaningful results in studies with correlated high dimensional data. The DFS consists of two steps: (i) decomposing the covariate space into disjoint subsets such that each of the subsets contains only uncorrelated covariates and (ii) identifying significant predictors by traditional feature selection within each covariate subset. We demonstrate through simulation studies that the DFS has superior practical performance over the Lasso type methods when multiple highly correlated covariates need to be retained. Application of the DFS is demonstrated through a study of bipolar disorders with correlated biomarkers.

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Section: Analysis Of the Bipolar Study For Femalesmentioning

confidence: 99%

Section: Data Introduction and Feature Space Decompositionmentioning

confidence: 99%